2456supp01 - Description of Statistical Design

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Willingness to Pay for Improved Water Quality in the Chesapeake Bay (Revised)

2456supp01 - Description of Statistical Design

OMB: 2010-0043

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Attachment 12: Description of Statistical Survey Design


The following represents an anticipated experimental design for survey implementation, along with the associated number of completed surveys that will be required. Part B of this supporting statement provides detail on the sampling design. The proposed design and sampling plan is based on standard design and sampling theory for choice experiments and population surveys, as outlined by Louviere et al. (2000), Kuhfeld (2009) and Dillman (2000). EPA notes that the anticipated experimental design described here is preliminary and it may be subject to refinements during design evaluations to account for issues such as dominant or dominated pairs, ecological feasibility, and to remove attribute combinations which do not provide information for estimation.


The purpose of the Chesapeake Bay survey is to calculate average per household parameters (e.g., willingness to pay and choice probabilities) within a given survey population. Additional analysis that differentiates per-household parameters may be conducted within groups of households which use or do not use the Chesapeake Bay.


Experimental design for the choice experiments

Based on focus groups and pretests, and guided by realistic ranges of attribute outcomes, the anticipated experimental design includes a fixed status quo or “no policy” option (Option A), and two multi-attribute choice options or alternatives, B and C. Choice options, named Option B and Option C, are characterized by three potential levels for environmental attributes and six different levels of annual household cost. Furthermore, the baseline environmental attribute levels vary over the split-sample experiments, with the Option A or status quo choice representing either a constant, declining, or improving baseline in the environmental attributes.


Different split-sample experiments will be produced in the three geographic divisions:


  1. Bay States: Maryland, Virginia, District of Columbia

  2. Watershed States: Delaware, New York, Pennsylvania, and West Virginia

  3. Other East Coast States: Vermont, New Hampshire, New Jersey, Massachusetts, Connecticut, Rhode Island, Maine, North Carolina, South Carolina, Georgia, and Florida

Within these geographic divisions, the split-sample experiment cells will collect more detailed information in the Bay states, as stipulated in Table A15-1.


Table A15-1. Split-sample design cells.


Geographic division

Baseline factor

Cell 1

Bay States

Declining

Cell 2

Bay States

Constant

Cell 3

Bay States

Improving

Cell 4

Watershed

Declining

Cell 5

Watershed

Constant

Cell 6

Watershed

Improving

Cell 7

Other East Coast

Declining

Cell 8

Other East Coast

Constant

Cell 9

Other East Coast

Improving


Options A and B are characterized by levels for the following five attributes:

  1. Change in water clarity in A and B (x1A; x1B) – 3 possible levels

  2. Change in blue crab abundance in A and B (x2A; x2B) – 3 possible levels

  3. Change in striped bass abundance in A and B (x3A; x3B) – 3 possible levels

  4. Change in oyster abundance in A and B (x4A; x4B) – 3 possible levels

  5. Change in lake condition in A and B (x5A; x5B) – 3 possible levels

  6. Cost in A and B (x6A; x6B) - 6 possible levels

This implies an experimental design characterized by [35×6] for each alternative, or [310×62] for alternatives A and B.


To construct a preliminary main effects design with 72 profiles that is sufficiently flexible to estimate alternative specific main effects and response patterns (i.e., a non-generic design), we begin with a 35x6 orthogonal fractional factorial design with 144 profiles. We then combined the elements of this design into pairs that would reflect trade-offs at the margin (i.e., improvements in the attributes that are attained at the cost of decrease in other environmental attributes and/or increase of the overall cost of the program). Finally, these pairs were blocked1 in such a way that variability of the environmental and cost attributes within a block would be maximized (and hence the main effects would not be confounded with the block effects). The result is a design with 72 profiles, with attributes labeled following the above notation, and levels indicated by integers 1...N, where N for each attribute is the number of levels identified above.


Following common practice in the environmental economics literature, we anticipate three choice questions per survey. This allows the 72 profiles to be included (orthogonally blocked) in 24 unique survey booklets, as illustrated in Table A15-2. The attribute levels applied within surveys are summarized in Table A15-3. Monte Carlo evidence suggests that 6 to 12 completed responses are required for each profile in order to achieve large sample statistical properties for choice experiments (Louviere et al. 2000, p. 104, citing Bunch and Batsell 1989). Following this guidance, the above design will require 24×12 = 288 completed surveys, or 12 completed surveys for each unique survey booklet. This will provide a total of 864 profile responses per cell.

Table A15-2. The set of 72 design profiles within each geographic division by baseline cell.

Booklet

X1A

X2A

X3A

X4A

X5A

X6A

X1B

X2B

X3B

X4B

X5B

X6B

1

1

2

1

2

2

6

2

1

1

1

2

5

1

2

1

2

3

3

3

3

1

2

3

1

1

1

3

3

3

1

2

2

3

3

2

2

2

1

2

1

1

2

2

2

4

1

1

3

1

2

5

2

2

2

1

3

1

5

1

2

1

3

2

6

2

3

3

3

2

3

3

3

3

2

3

2

2

3

2

2

2

1

2

2

1

2

1

3

2

5

3

2

3

1

2

3

6

1

1

1

2

3

2

3

3

1

3

3

1

1

2

1

3

3

3

2

4

1

1

2

2

3

3

1

3

3

2

1

4

4

2

3

3

3

1

1

2

2

3

3

2

1

4

3

1

2

2

3

5

3

1

2

1

2

2

5

1

1

3

1

1

4

1

2

1

1

2

4

5

2

3

3

2

3

6

1

3

3

2

1

2

5

3

2

3

2

3

3

3

3

2

3

2

3

6

1

1

2

1

3

3

2

2

3

1

3

4

6

2

1

1

2

2

3

2

3

1

2

1

5

6

3

1

3

3

1

6

3

2

3

2

1

5

7

1

1

2

2

3

2

2

1

3

2

1

4

7

1

3

3

1

2

1

1

2

3

3

2

2

7

2

2

1

3

1

3

3

2

1

1

1

1

8

2

1

1

1

3

2

2

1

3

1

2

4

8

2

2

2

2

3

1

2

2

2

3

2

1

8

3

3

2

3

1

4

1

3

1

3

1

3

9

1

1

2

3

3

5

1

3

2

3

2

6

9

1

2

2

1

1

6

1

1

1

1

1

3

9

3

1

1

2

2

1

2

1

3

2

2

3

10

1

2

1

3

1

1

1

3

1

3

2

3

10

1

3

3

2

3

3

2

3

3

3

3

5

10

3

1

2

1

2

6

1

1

2

1

1

3

11

2

1

3

1

3

1

2

2

2

1

3

1

11

2

3

1

2

2

6

1

3

2

2

2

5

11

3

2

3

1

3

3

3

2

3

2

2

4

12

2

2

1

1

1

3

2

1

1

3

1

4

12

3

1

2

2

2

1

2

2

2

2

2

1

12

3

3

2

3

3

2

3

2

3

3

3

1

13

1

3

1

1

3

1

3

3

1

2

3

4

13

2

2

2

1

3

6

2

2

2

2

1

4

13

2

3

1

3

2

5

3

1

1

3

2

3

14

1

3

1

2

3

1

2

2

1

2

3

2

14

2

1

2

1

2

4

2

1

2

3

1

6

14

3

2

2

2

1

6

3

1

3

2

1

5

15

1

2

1

3

3

5

3

2

1

1

3

4

15

2

3

2

3

2

3

2

3

3

2

2

4

15

3

1

1

2

1

6

3

1

1

1

2

6

16

1

2

3

1

1

5

1

3

2

1

1

6

16

2

2

3

3

3

6

3

2

3

1

3

5

16

3

1

1

2

3

1

3

3

1

1

3

2

17

1

1

3

2

2

5

1

2

3

2

1

6

17

2

3

2

1

3

6

1

3

3

1

3

6

17

3

1

1

3

1

6

2

1

1

1

1

4

18

1

2

2

2

3

3

1

1

1

2

3

1

18

2

3

3

1

2

2

2

2

3

2

2

3

18

3

2

2

2

1

2

3

2

3

3

1

3

19

1

1

3

3

2

1

3

1

3

3

1

2

19

1

3

2

3

1

4

1

2

2

2

1

2

19

3

2

2

1

3

5

3

3

1

1

3

6

20

1

2

2

3

2

2

1

2

1

3

3

4

20

2

3

2

2

1

5

2

2

1

2

1

2

20

3

3

3

3

3

4

2

3

3

3

1

2

21

2

3

2

1

1

1

1

3

3

1

1

1

21

3

2

3

2

2

6

3

2

3

1

1

3

21

3

3

1

3

3

4

3

2

1

3

2

2

22

1

3

1

1

1

1

2

3

1

1

2

3

22

2

1

3

3

3

6

1

1

3

1

3

2

22

2

3

2

3

3

5

1

2

2

3

3

4

23

2

1

2

2

1

5

2

1

1

1

1

2

23

3

1

1

3

3

5

1

1

2

3

3

4

23

3

2

2

1

2

4

3

3

2

1

1

3

24

1

2

3

1

2

5

1

1

3

3

2

6

24

1

3

1

2

1

2

3

3

1

2

2

4

24

3

3

2

1

1

5

3

3

1

1

2

5


Table A15-3: Attribute Levels Included in Each Survey Version. 

Attribute

Attribute Levels

Baseline

1

2

3

4

5

6

Declining Baseline

Water Clarity (feet)

1.5

3

5

6

-

-

-

Striped Bass (millions)

21

24

28

30

-

-

-

Blue Crab (millions)

235

250

280

312

-

-

-

Oysters (tons)

2,800

3,300

4,300

5,250

-

-

-

Low Algae Level Lakes

2,300

2,900

3,300

3,850

-

-

-

Annual Household Cost

-

$20

$40

$60

$180

$250

$500

 

Constant Baseline

Water Clarity (feet)

3

3

5

6

-

-

-

Striped Bass (millions)

24

24

28

30

-

-

-

Blue Crab (millions)

250

250

280

312

-

-

-

Oysters (tons)

3,300

3,300

4,300

5,250

-

-

-

Low Algae Level Lakes

2,900

2,900

3,300

3,850

-

-

-

Annual Household Cost

-

$20

$40

$60

$180

$250

$500

 

Improving Baseline

Water Clarity (feet)

4

4

5

6

-

-

-

Striped Bass (millions)

26

26

30

35

-

-

-

Blue Crab (millions)

260

260

312

340

-

-

-

Oysters (tons)

4,300

4,300

5,250

6,500

-

-

-

Low Algae Level Lakes

3,100

3,350

3,600

3,850

-

-

-

Annual Household Cost

-

$20

$40

$60

$180

$250

$500


Realized Sample Sizes for Maximum Acceptable Sampling Error

The goal of the choice experiment is to estimate regression coefficients from mixed or conditional logit models that may be used to estimate willingness to pay for multi-attribute policy alternatives, or the likelihood of choosing a given multi-attribute alternative, following standard random utility modeling procedures (Haab and McConnell 2002). Hence, the sample size requirements are determined by the accuracy of the parameter estimates in the WTP models.

The resulting sample design will be a single stage stratified sample. No clustering (multiple stages of selection) will be necessary. Unequal probabilities of selection will result in different geographic divisions defined in Part B, Section 2 “Survey design”, and lead to varying sampling weights, as demonstrated in Table A15-4 (assuming that the design contains 9 cells). Due to these varying weights, under assumptions of constant response rate and fixed sample size, the expected design effect due to differential baseline weights is 1.75. The realized design effect will likely be higher due to extra variability of weights within cells due to non-response adjustments.


Table A15-4. Sample size and accuracy projections.

Geographic division

Population size

Expected sample size

Expected weights

Standard error, 50% incidence

Standard error, 10% incidence

Bay States

5,479,176

864

6341

0.017

0.010

Watershed

13,442,787

864

15,559

0.017

0.010

Other East Coast

25,431,478

864

29,434

0.017

0.010

Overall

44,353,441

2,592

17,081

0.011

0.007

Source: The household population size for each region was obtained from U.S. Census Bureau (2012). 2010 Census Summary File 1. Retrieved May 31, 2012 from http://factfinder2.census.gov/.


The maximum acceptable sampling error for predicting response probabilities (the likelihood of choosing a given alternative) in the present case is ±10%, assuming a true response probability of 50% associated with a utility indifference point. Given the survey population size, this level of precision requires a minimum sample size of approximately 96 observations. The number of observations (completed surveys) required to obtain large sample properties for the choice experiment design provide more than sufficient observations to obtain this required precision for population parameters.


Projected sample sizes given the potential non-response

Survey non-response is a common phenomenon. The sample design must be proactive and account for the potential non-response. Based on recent experience with surveys of similar nature, EPA expects the response rate for the Chesapeake Bay survey to be close to 30%. Additionally, the expected eligibility rate for a mail survey is 92%, and accounts for vacant, seasonal, non-existent, and otherwise ineligible units. The projected number of required mailings is given in Table A15-5 for different scenarios (response rates of 20% and 30%) and different sample size determination methods (expected number of mailings vs. the number of mailings that ensures 90% probability of reaching the cell target sample size).


Table A15-5. Required sample size.

Target cell size: n=288

r=20% response rate

r=30% response rate

Mean projection, n/r

90% prob to achieve cell size

Mean projection, n/r

90% prob to achieve cell size

Required cell size

1,565

1,672

1,043

1,111

District of Columbia

229

244

152

162

Maryland

1,848

1,974

1,231

1,311

Virginia

2,619

2,798

1,745

1,857

Delaware

120

128

80

85

New York

2,556

2,731

1,703

1,813

Pennsylvania

1,753

1,873

1,168

1,243

West Virginia

267

285

178

189

Connecticut

253

270

170

180

Florida

1,370

1,465

913

973

Georgia

662

707

441

470

Maine

103

110

69

73

Massachusetts

470

502

313

334

New Hampshire

96

102

64

68

New Jersey

593

634

395

421

North Carolina

691

739

461

491

Rhode Island

76

82

51

54

South Carolina

333

355

222

236

Vermont

47

51

32

34

Total:

14,086

15,050

9,388

9,994


The sample size required for 90% probability of achieving the cell size is computed as the 90-th percentile of the negative binomial distribution with success probability equal to the response rate and the number of successes equal to the target cell size.

1 EPA assigned each profile to an independent subset, or “block” of profiles. Blocking reduces the number of profiles each respondent sees, thus reducing respondent burden.

41


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